In this work, we propose FedCG, a novel federated learning method that leverages conditional generative adversarial networks [1]} to achieve high-level privacy protection resisting DLG attack while still maintaining competitive model performance compared with baseline FL methods. More specifically, FedCG is similar to FedSplit in that it decomposes each client's local network into a private extractor and a public classifier, and keeps the extractor local to protect privacy. The novel part of FedCG is that it shares clients' generators in the place of extractors with the server for aggregating common knowledge aiming to enhance the performance of clients' local networks. This strategy has two immediate advantages. First, the possibility of clients' data leakage is significantly reduced because no model that directly contacts with original data is exposed, as compared to FL methods in which the server has full access to clients' local networks (e.g., FedAvg and FedProx). Second, clients can integrate common knowledge embedded in the global generator (aggregated on the server) into their local extractors for improving model performance, as compared to FedSplit methods that train extractors with only local data. An additional benefit brought by shared generators is that the server can aggregate local generators and classifiers using knowledge distillation [2]} without accessing any public data.
